How Freight Moves: Estimating Mileage and 

Routes Using an Innovative GIS Tool

Bureau of Transportation Statistics

Technical Report

U.S. Department of Transportation

Research and Innovative Technology Administration

June 2007

TR - 001

by Stephen M. Lewis and 

Felix Ammah-Tagoe, Ph.D.

The Bureau of Transportation 

Statistics (BTS) has developed 

an innovative software tool, called 

GeoMiler, that is helping researchers better estimate freight travel. 

GeoMiler is being used to compute mileages along likely routes 

for the nearly 6 million freight shipments expected to be reported in 

the 2007 Commodity Flow Survey 

(CFS), the nation’s largest survey of freight movements. These 

computations are used in estimating modal ton-miles of freight—a 

key measure for understanding 

the use and performance of our 

nation’s freight transportation 

system. 

BTS, part of the Research and 

Innovative Technology Administration (RITA) of the U.S. Department of Transportation (USDOT), 

developed GeoMiler using current 

Geographic Information System 

(GIS) technology to assign routes 

and calculate mileage from the 

true origin to the true destination 

of each freight shipment even 

when more than one freight mode 

is used. While developed for use 

in the CFS, the tool’s integration 

of core GIS technology and its 

modeling approach can be used 

for any multimodal freight movement at all geographic levels.

Background

The Commodity Flow Survey, a 

nationwide survey of U.S. businesses that generate freight 

shipments, is the primary source 

of national data on the fl ow of 

goods—from where to where, how 

far, and by what means. The 2007 

survey, sponsored by BTS and 

conducted by the Census Bureau, 

is currently underway; it covers 

100,000 U.S. business establishments primarily engaged in manufacturing, mining, and wholesale 

activities. Visit www.bts.gov/cfs for 

additional information about the 

survey.

For the prior CFSs, conducted in 

1993, 1997, and 2002, shipment 

distances were estimated by using 

a set of routing modules that are 

now dated and do not fi t within the 

current USDOT Enterprise Architecture. There were individual 

modules for air cargo, surface 

modes, and exports. As a result, 

the CFS records had to be processed multiple times. Because 

GIS technology was not used, the 

models relied on fl at ASCII-fi le 

representations of the transportation networks. These networks 

were cumbersome to maintain. 

Without the GIS component, there 

was no way to visualize on a map 

the routes that were generated.

For the 2007 CFS, BTS wanted 

a tool that met CFS’ operational 

requirements while being easy to 

maintain and upgrade. This effort 

was part of a major performance 

push to improve overall effi ciency, 

methods, and quality of mileage 

information in the 2007 CFS.

Why the Need for Mileage 

Estimation?

CFS shipment mileage is estimated by BTS after the survey 

data have been collected from 

respondents. The respondents 

were shippers and not transportation providers (carriers). Shippers know the actual origin and 

destination of their shipments but 

may know little about the specifi c 

modes of transportation used by 

carriers, the ultimate routes taken, 

and the distance traveled. The 

survey asks for origin and destination ZIP Code and the mode 

sequence (e.g., highway-railhighway) used for each shipment. 
BTS Technical Report

From this information, BTS must calculate the mileage traveled by single mode (highway, rail, water, air, 

parcel, and pipeline) and by many multimodal combinations. 

Few, if any, off-the-shelf national-level multimodal 

transportation networks are available for use in a 

GIS. In addition, there are almost no commercial 

routing modules capable of seamlessly handling 

intermodal transfers in the specifi c modal sequence 

provided by shippers. For example, routing a grain 

shipment by truck, rail, inland waterway, and truck is 

not a simple shortest path problem. It requires routing 

logic that allows the shipment to transfer at commodity-appropriate intermodal terminals and water docks. 

This routing logic is built into GeoMiler. 

The absence of a readily available commercial solution for such multimodal routing led BTS to develop 

the GeoMiler network routing tool, using current GIS 

technology and robust spatial network data. It integrates map visualization features with route solvers 

to handle many alternative multimodal combinations 

from the following list of modes represented in the 

CFS:

parcel delivery, courier, U.S. postal post;

private truck;

for-hire truck;

railroad;

shallow-draft (Inland Water) Vessel;

deep-draft (Ocean) Vessel;

great Lakes vessel (generated by routing 

models);

air;

pipeline;

unknown (don′t know or missing);

other; and

multimode (any combination of the above).

GIS Platform and System Architecture

The operational requirements for accurately estimating mileages for the nearly 6 million shipments, 

of which over one-tenth involved some multimodal 

transfer, required a multitier logical and physical 

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Input

Data

Pre-Processor

(Corrects

geographic

information)

Mileage Solver

(Estimates 

miles for each

shipment)

Post-Processor

(Prepares 

output data 

for submission

to Census)

Map Interface

(Verifies and

corrects paths

for problem

records)

Output

Data

DATA PROCESSOR

ROUTE SOLVER

MAPPING INTERFACE

Figure 1 shows the three GeoMiler components – data processor, route solver, and map interface. The data 

pre-processor hosts all the input survey data received from the Census Bureau, and the post-processor returns 

the output data to the Census Bureau. The route solver performs the mileage calculations for all the freight 

modes. The mapping interface allows visualization of routes for any shipment records that have problematic 

input data and allows shipment paths to be checked, verifi ed, and corrected.

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of 

Transportation Statistics.

Figure 1. GeoMiler System Flow
BTS Technical Report

design (fi gure 1). This design allowed data pre-processing separate from the route solver, modeling 

logic, and mapping interface. This system design and 

the development platform meet USDOT’s Enterprise 

Architecture requirements.

In this multitier confi guration, the entire tool, 

including the data pre-processor and post-processor, was developed with a software tool that uses 

the BASIC programming language.

With the exception of pipelines, the route solver 

module for all modes – highway, rail, water, air, 

parcel, and multimodal shipments – was based 

on the analytical logic and route solvers from off 

the shelf GIS software. Due to restrictions on use 

of the national pipeline network, GeoMiler calculates great circle distance (GCD) for all pipeline 

shipments.

The mapping visualization interface was also 

based on off the shelf GIS software, which was 

customized to allow the assigned paths to be 

checked and corrected.

The use of GIS in GeoMiler allows the most likely 

paths taken by shipments from ZIP Code to ZIP Code 

to be displayed on a computer screen. GIS was used 

to create a series of programming and modeling routines that allowed for analysis of the individual freight 

transportation networks for roads, rail, waterways, 

and air cargo. More importantly, it allowed for analysis across all of these modes. Specifi cally, GeoMiler 

creates connectivity rules among the different networks, models the impedance factors based on cost 

descriptors and network hierarchies, and defi nes the 

routing logic that uses these impedances. Each link 

in the multimodal network is assigned an impedance 

value, which represents the likelihood of fl ow on each 

link. The higher the impedance value, the less likely 

the link will be selected for the path. By leveraging 

and customizing GIS technology, GeoMiler allows estimation of the optimal paths for all modes and allows 

each path to be displayed for analysis or decision 

making.

Developing Multimodal Spatial Network 

Data

GeoMiler uses a series of spatially referenced individual transportation networks (highway, rail, waterway, and airway) linked at points (airports, seaports, 

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and intermodal transfer facilities) with straight line 

access and egress connections that we call “spatial joins.”1

 This approach makes it unnecessary to 

merge the individual mode-specifi c networks into 

one large integrated multimodal network. Keeping 

the modal networks separate and connecting them 

with spatial joins allow distance estimation for both 

single-mode shipments and any combination of 

multimodal shipments from any ZIP Code to any ZIP 

Code.

It also allows calculations for export shipments by 

any mode to Canada and Mexico as well as by air 

freight and maritime vessel to all U.S. overseas trading partners. 

This spatial network database includes ZIP Code 

centroids, the U.S. highway network, Canadian and 

Mexican highway and rail networks, national rail network, national waterway and docks network (including inland, coastal, and Great Lakes), global seaway 

network, intermodal truck-rail-ports terminals, and 

North American border crossing locations. BTS constructed this geospatial multimodal network from the 

following sources: 

Highways and Roads – Highway route data 

from a commercial source provided coverage of 

roads in the United States. Additional sources 

were used for Canada and Mexico.

Railroads – Federal Railroad Administration 

(FRA) maintains the North American rail network 

used in the Rail Waybill data (enhanced with 

Waybill ownership, trackage rights, and rail densities).

Airports and Airways – Federal Aviation Administration sources provided location information 

for large, medium, and small hubs that handle 

freight. For Alaska, it included all hub and nonhub airports. A domestic and international airway 

routes network was created from offi cial air cargo 

data from the BTS Offi ce of Airline Information 

(OAI). 

1

 Transportation facilities (e.g., airports, highway/rail transfer yards) 

can be extensive and contain many miles of roads or rail. Rather than 

navigating these labyrinths of rail and asphalt when connecting two 

or more different freight transportation modes, which is cumbersome 

and time consuming, GeoMiler represents each of these facilities as a 

single point. The “shortest path solvers” function in GeoMiler then uses 

straight-line links, called “spatial joins,” to connect theses points to the 

appropriate transportation mode(s).

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BTS Technical Report

Waterways – Routable network maintained by 

the U.S. Army Corps of Engineers (USACE) and 

distributed by BTS.

Water Ports – USACE sources, including dockto-dock shipment location and commodity information for all docks on the inland waterways and 

seaports.

Intermodal Facilities – database created and 

maintained by BTS. 

Zip Code Centroids – United States Postal Service and a commercial source.

Complementary Input Data

Working together through interagency cooperation, 

BTS received offi cial freight data, including information not available for public use, from the FRA and 

the USACE to support the modeling, validation, and 

selection of the optimal paths. The major primary 

complementary data sources included:

FRA Waybill Sample Data – used to determine 

the link densities, interlining, and rail station use; 

USACE waterborne traffi c data – used to determine the most likely docks by commodity for 

multimodal shipments involving the U.S. waterway system;

Census Bureau U.S.-international trade data 

– used to select and validate the most likely air 

and sea routes for all trading partners, including 

oceanborne shipments to landlocked countries; 

BTS OAI air cargo data – used to control cargo 

traffi c among airport pairs for both domestic traffi c 

and exports to all countries; 

BTS TransBorder and Border Crossing data 

– used to select the most likely crossing points for 

exports into Canada and Mexico by truck and rail.

Modeling Simulated Pathfi nder Routes

GeoMiler’s basic modeling routines are straight forward. With data on specifi c origins and destinations 

and mode sequence, the tool determines the set of 

spatial links and nodes that make up the “best possible” or optimal route between the two locations (fi gure 2). This selected route simulates a typical likely 

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path within the multimodal network that optimizes a 

set of pre-defi ned conditions that are mode and commodity specifi c.

For the highway (both for-hire and private) and water 

(inland shallow draft, deep draft, and Great Lakes) 

components, the tool fi nds the least-impedance path 

from origin point to destination point over the highway 

and waterway networks and then sums the lengths of 

individual links on these paths. 

For rail and rail-inclusive intermodal, the tool estimates a least-impedance path based on railroad specifi c operating paths and selects the most likely route. 

Rail shipments are routed over the latest FRA rail 

network updated with the latest ownership changes, 

abandonments, trackage rights, interlining, and track 

density.

For air shipments, GeoMiler fi nds the least-impedance truck routes to the origin airport and from the 

destination airport. It then uses airport-to-airport 

traffi c availability to estimate the most likely route, 

giving priority to direct fl ights between major hub airports, single carrier routes (circuity constrained), and 

maximum air cargo lift from airports, while controlling 

for the ratio between the truck mileage and the air 

mileage.

Because air traffi c does not physically cross the 

U.S. border at designated gateways, as do the other 

modes such as rail, GeoMiler measures the U.S. portion of an international fl ight by extracting that portion 

of the fl ight that extends from the U.S. airport of exit 

to the point where the fl ight path intersects the U.S. 

border.

GeoMiler Deployed

As deployed, the GIS-based GeoMiler uses current 

generation programming languages with standard 

commercial GIS software to provide a fast and effi cient data processing module with the capability to 

estimate and verify shipment mileages. It offers a 

dynamic modal routing tool able to process data and 

generate shipment distances for single modes and 

multimodal shipments.

It also offers a very user-friendly interface with a 

consistent diagnostic tool for reconciling records 

with problematic origin and destination state and ZIP 
BTS Technical Report

Code information. New route viewing capability, including multimodal routes and information on choice 

of intermodal facilities, allows user-analysts to make 

informed choices when the input modal information 

needs correction.

While GeoMiler was developed for the CFS, the approach used in creating the geospatial databases and 

modeling the multimodal movements could be leveraged for other applications.

Figure 2. Illustration of an Intermodal Freight Shipment Path

Figure 2 illustrates the routing of a multimodal shipment from origin to destination. In this example, the 

shipment leaves the true origin by rail, travels to a transfer facility where the cargo is transferred to a vessel, 

then travels by water to another transfer facility close to the destination, from where it is transferred to truck 

and travels on the highway system to the shipment’s true destination.

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of 

Transportation Statistics.

Rail network 

Highway network 

Water network

Transfer facility

Transfer facility

Spatial joins 

Spatial joins

ORIGIN

DESTINATION
BTS Technical Report

For related data and reports visit www.bts.gov

A BTS contract team under the management of Stephen Lewis and 

Michael Margreta developed GeoMiler. The software developing, programming, and data expertise of Adhi Dipo, Fahim Mohamed, Raquel 

Wright, and Derald Dudley were critical. The freight modeling and CFS 

insights were provided by Felix Ammah-Tagoe. The Federal Railroad 

Administration and the Waterborne Commerce Statistics Center of the 

Army Corps of Engineers provided critical complementary data and 

technical support that made this tool possible.

The Bureau of Transportation Statistics is a component of USDOT’s 

Research and Innovative Technology Administration.

For questions about this or other BTS reports, call 

1-800-853-1351, email answers@bts.gov or visit 

www.bts.gov.

Data - 

NTAD–National Transportation Atlas Database

Commodity Flow Survey—value, weight, and ton-miles by 

commodity, mode, and origins and destinations

Transborder Freight Data—monthly release of U.S.-Canada 

and U.S.-Mexico trade data

Border Crossing/Entry Data—monthly data on 

incoming vehicle crossings from Canada and Mexico 

Reports - 

America’s Container Ports: Delivering the Goods

North American Freight Transportation 2006

Freight in America

America’s Freight Transportation Gateways 2004

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Acknowledgments

Great Lakes -> Inland Water -> Truck: Chicago to Haines City, FL

Figure 3. Example of a GeoMiler Path Determination

Figure 3 is a snapshot view from the mapping interface. In this case, the input origin is Chicago, Illinois and 

the destination is Haines City, Florida. The input mode sequence is water followed by highway. Using this 

information, GeoMiler calculates a path in which the shipment leaves Chicago, travels by inland waterways 

through the port of Mobile, Alabama, across the Gulf of Mexico to a port in Florida, and then continues by 

truck to its fi nal destination in Haines City, Florida, traveling a distance of 1,849 miles.

SOURCE: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of 

Transportation Statistics.